Showing papers on "Adaptive filter published in 2017"

Design and Analysis of Robust Active Damping for LCL Filters Using Digital Notch Filters

Abstract: Resonant poles of LCL filters may challenge the entire system stability especially in digital-controlled pulse width modulation (PWM) inverters. In order to tackle the resonance issues, many active damping solutions have been reported. For instance, a notch filter can be employed to damp the resonance, where the notch frequency should be aligned exactly to the resonant frequency of the LCL filter. However, parameter variations of the LCL filter as well as the time delay appearing in digital control systems will induce resonance drifting, and thus break this alignment, possibly deteriorating the original damping. In this paper, the effectiveness of the notch filter-based active damping is first explored, considering the drifts of the resonant frequency. It is revealed that when the resonant frequency drifts away from its nominal value, the phase lead or lag introduced by the notch filter may make itself fail to damp the resonance. Specifically, the phase lag can make the current control stable despite of the resonant frequency drifting, when the grid current is fed back. In contrast, in the case of an inverter current feedback control, the influence of the phase lead or lag on the active damping is dependent on the actual resonant frequency. Accordingly, in this paper, the notch frequency is designed away from the nominal resonant frequency to tolerate the resonance drifting, being the proposed robust active damping. Simulations and experiments performed on a 2.2-kW three-phase grid-connected PWM inverter verify the effectiveness of the proposed design for robust active damping using digital notch filters.

Adaptive adjustment of noise covariance in Kalman filter for dynamic state estimation

Abstract: Accurate estimation of the dynamic states of a synchronous machine (e.g., rotor's angle and speed) is essential in monitoring and controlling transient stability of a power system. It is well known that the covariance matrixes of process noise (Q) and measurement noise (R) have a significant impact on the Kalman filter's performance in estimating dynamic states. The conventional ad-hoc approaches for estimating the covariance matrixes are not adequate in achieving the best filtering performance. To address this problem, this paper proposes an adaptive filtering approach to adaptively estimate Q and R based on innovation and residual to improve the dynamic state estimation accuracy of the extended Kalman filter (EKF). It is shown through the simulation on the two-area model that the proposed estimation method is more robust against the initial errors in Q and R than the conventional method in estimating the dynamic states of a synchronous machine.

Lithium-Ion Battery Pack State of Charge and State of Energy Estimation Algorithms Using a Hardware-in-the-Loop Validation

Abstract: An adaptive H infinity filter approach is proposed to estimate the multistates including state of charge (SOC) and state of energy (SOE) for a lithium-ion battery pack. In the proposed approach, the covariance matching technique is used to adaptively update the covariance of system and observation noises and the recursive least square method is used to identify the battery model parameters in real time. The hardware-in-the-loop (HIL) platform for battery charge/discharge is set up to evaluate the accuracy and robustness of the SOC and the SOE estimation and compare the proposed approach with the multistate estimators using an extended Kalman filter and an H infinity filter. The experimental results indicate that the adaptive H infinity filter-based estimator is able to estimate the battery states in real time with the highest accuracy among the three filters.

Volterra and Wiener Equalizers for Short-Reach 100G PAM-4 Applications

Abstract: Unlike ultralong coherent optical systems that seriously suffer from fiber nonlinearities, short-reach noncoherent systems such as data center interconnections, which utilize small, cheap, and low-bandwidth components, are sensitive to nonlinearities that are mainly produced by devices responsible for electrical signal amplification, modulation, and demodulation. One of the most promising schemes for these applications is the four-level pulse amplitude modulation format combined with intensity modulation and direct detection; however, it can be significantly degraded by linear and nonlinear intersymbol interference. Linear and nonlinear signal degradation can efficiently be handled by different types of equalizers. In many cases, the straightforward linear equalizer cannot lower the error rate at the acceptable level. Therefore, much stronger equalizers based on nonlinear models such as the Volterra series are proposed. Volterra filter that can also be orthogonalized by the Wiener model is well described in the existing literature, and, in this paper, we investigate the most critical points related to high-speed Volterra filter design and implementation. Several experiments are carried out in order to indicate filter requirements/complexity, acquisition, and stability. We also provide a simple guidance for filter complexity reduction and useful hints for channel acquisition.

Maximum-Likelihood Adaptive Filter for Partially Observed Boolean Dynamical Systems

Abstract: We present a framework for the simultaneous estimation of state and parameters of partially observed Boolean dynamical systems (POBDS). Simultaneous state and parameter estimation is achieved through the combined use of the Boolean Kalman filter and Boolean Kalman smoother, which provide the minimum mean-square error state estimators for the POBDS model, and maximum-likelihood (ML) parameter estimation; in the presence of continuous parameters, ML estimation is performed using the expectation–maximization algorithm. The performance of the proposed ML adaptive filter is demonstrated by numerical experiments with a POBDS model of gene regulatory networks observed through noisy next-generation sequencing (RNA-seq) time series data using the well-known p53-MDM2 negative-feedback loop gene regulatory model.

Global Optimization of Microwave Filters Based on a Surrogate Model-Assisted Evolutionary Algorithm

Abstract: Local optimization is a routine approach for full-wave optimization of microwave filters. For filter optimization problems with numerous local optima or where the initial design is not near to the optimal region, the success rate of the routine method may not be high. Traditional global optimization techniques have a high success rate for such problems, but are often prohibitively computationally expensive considering the cost of full-wave electromagnetic simulations. To address the above challenge, a new method, called surrogate model-assisted evolutionary algorithm for filter optimization (SMEAFO), is proposed. In SMEAFO, considering the characteristics of filter design landscapes, Gaussian process surrogate modeling, differential evolution operators, and Gaussian local search are organized in a particular way to balance the exploration ability and the surrogate model quality, so as to obtain high-quality results in an efficient manner. The performance of SMEAFO is demonstrated by two real-world design cases (a waveguide filter and a microstrip filter), which do not appear to be solvable by popular local optimization techniques. Experiments show that SMEAFO obtains high-quality designs comparable with global optimization techniques but within a reasonable amount of time. Moreover, SMEAFO is not restricted by certain types of filters or responses. The SMEAFO-based filter design optimization tool can be downloaded from http://fde.cadescenter.com .

Non-invasive fetal monitoring: a maternal surface ECG electrode placement-based novel approach for optimization of adaptive filter control parameters using the LMS and RLS algorithms

Abstract: This paper is focused on the design, implementation and verification of a novel method for the optimization of the control parameters (such as step size μ and filter order N) of LMS and RLS adaptive filters used for noninvasive fetal monitoring. The optimization algorithm is driven by considering the ECG electrode positions on the maternal body surface in improving the performance of these adaptive filters. The main criterion for optimal parameter selection was the Signal-to-Noise Ratio (SNR). We conducted experiments using signals supplied by the latest version of our LabVIEW-Based Multi-Channel Non-Invasive Abdominal Maternal-Fetal Electrocardiogram Signal Generator, which provides the flexibility and capability of modeling the principal distribution of maternal/fetal ECGs in the human body. Our novel algorithm enabled us to find the optimal settings of the adaptive filters based on maternal surface ECG electrode placements. The experimental results further confirmed the theoretical assumption that the optimal settings of these adaptive filters are dependent on the ECG electrode positions on the maternal body, and therefore, we were able to achieve far better results than without the use of optimization. These improvements in turn could lead to a more accurate detection of fetal hypoxia. Consequently, our approach could offer the potential to be used in clinical practice to establish recommendations for standard electrode placement and find the optimal adaptive filter settings for extracting high quality fetal ECG signals for further processing. Ultimately, diagnostic-grade fetal ECG signals would ensure the reliable detection of fetal hypoxia.

Online Fault Diagnosis of Motor Bearing via Stochastic-Resonance-Based Adaptive Filter in an Embedded System

Abstract: Digital signal processing algorithms are widely adopted in motor bearing fault diagnosis. However, most algorithms are developed on desktop platforms, and their focus is on the analysis of offline captured signals. In this paper, a simple and easily implemented algorithm running on an embedded system is proposed for the online fault diagnosis of motor bearing. The core part of the algorithm is a stochastic-resonance-based adaptive filter that realizes signal denoising and adaptation of the filter coefficient. Processed by the filter, the period of the purified signal is obtained, and then the fault type of the motor bearing is identified. The proposed method has distinct merits, such as low computational cost, online implementation, contactless measurement, and availability for various speed motors. This paper provides a simple, flexible, and effective solution for conducting motor bearing diagnosis on an embedded/portable device. The algorithm proposed is validated by a brushless dc motor and a brushed dc motor fabricating with defective/healthy support bearings.

LMS Adaptive Filters for Noise Cancellation: A Review

Abstract: This paper reviews the past and the recent research on Adaptive Filter algorithms based on adaptive noise cancellation systems. In many applications of noise cancellation, the change in signal characteristics could be quite fast which requires the utilization of adaptive algorithms that converge rapidly. Algorithms such as LMS and RLS proves to be vital in the noise cancellation are reviewed including principle and recent modifications to increase the convergence rate and reduce the computational complexity for future implementation. The purpose of this paper is not only to discuss various noise cancellation LMS algorithms but also to provide the reader with an overview of the research conducted.

Performance Enhancement of MEMS-Based INS/UWB Integration for Indoor Navigation Applications

Abstract: Inertial navigation system (INS) has an increasingly important role in indoor navigation, which mainly uses inertial measurement units based on a micro electro mechanical system (MEMS) to acquire data, and which is independent of the external environment. However, INS has serious accumulated errors, and thus, it was often integrated with wireless location systems (WLSs), such as ultra wideband (UWB) system, in order to enhance the position performance. Namely, MEMS-based inertial sensors have the problem of random errors. Besides, a UWB system is vulnerable to external environment conditions, such as the non-line-of-sight (NLOS) factor and multipath effects, and thus, many outliers are produced. In order to improve the overall performance of the INS/UWB system, this paper proposes the three-tier approach based on: 1) analysis and pre-filtering of random errors of MEMS-based inertial sensors, and use of a complementary filter to provide attitude information of navigation system; 2) use of the anti-magnetic ring (AMR) to eliminate the outliers from the UWB system in NLOS environment; and 3) improvement of positioning accuracy at information fusion level using the double-state adaptive Kalman filter. The proposed approach was verified by experiments that included AMR test and filter test. The obtained results have validated the proposed method efficiency.

A Phonocardiographic-Based Fiber-Optic Sensor and Adaptive Filtering System for Noninvasive Continuous Fetal Heart Rate Monitoring

Abstract: This paper focuses on the design, realization, and verification of a novel phonocardiographic- based fiber-optic sensor and adaptive signal processing system for noninvasive continuous fetal heart rate (fHR) monitoring. Our proposed system utilizes two Mach-Zehnder interferometeric sensors. Based on the analysis of real measurement data, we developed a simplified dynamic model for the generation and distribution of heart sounds throughout the human body. Building on this signal model, we then designed, implemented, and verified our adaptive signal processing system by implementing two stochastic gradient-based algorithms: the Least Mean Square Algorithm (LMS), and the Normalized Least Mean Square (NLMS) Algorithm. With this system we were able to extract the fHR information from high quality fetal phonocardiograms (fPCGs), filtered from abdominal maternal phonocardiograms (mPCGs) by performing fPCG signal peak detection. Common signal processing methods such as linear filtering, signal subtraction, and others could not be used for this purpose as fPCG and mPCG signals share overlapping frequency spectra. The performance of the adaptive system was evaluated by using both qualitative (gynecological studies) and quantitative measures such as: Signal-to-Noise Ratio—SNR, Root Mean Square Error—RMSE, Sensitivity—S+, and Positive Predictive Value—PPV.

Swarm and evolutionary computing algorithms for system identification and filter design: A comprehensive review

Abstract: An exhaustive review on the use of structured stochastic search approaches towards system identification and digital filter design is presented in this paper. In particular, the paper focuses on the identification of various systems using infinite impulse response adaptive filters and Hammerstein models as well as on the estimation of chaotic systems. In addition to presenting a comprehensive review on the various swarm and evolutionary computing schemes employed for system identification as well as digital filter design, the paper is also envisioned to act as a quick reference for a few popular evolutionary computing algorithms.

A Multisensor Multi-Bernoulli Filter

Abstract: In this paper, we derive a multisensor multi-Bernoulli (MS-MeMBer) filter for multitarget tracking. Measurements from multiple sensors are employed by the proposed filter to update a set of tracks modeled as a multi-Bernoulli random finite set. An exact implementation of the MS-MeMBer update procedure is computationally intractable. We propose an efficient approximate implementation by using a greedy measurement partitioning mechanism. The proposed filter allows for Gaussian mixture or particle filter implementations. Numerical simulations conducted for both linear-Gaussian and nonlinear models highlight the improved accuracy of the MS-MeMBer filter and its reduced computational load with respect to the multisensor cardinalized probability hypothesis density filter and the iterated-corrector cardinality-balanced multi-Bernoulli filter especially for low probabilities of detection.

Combination of adaptive vector median filter and weighted mean filter for removal of high-density impulse noise from colour images

Abstract: In this study, a combination of adaptive vector median filter (VMF) and weighted mean filter is proposed for removal of high-density impulse noise from colour images. In the proposed filtering scheme, the noisy and non-noisy pixels are classified based on the non-causal linear prediction error. For a noisy pixel, the adaptive VMF is processed over the pixel where the window size is adapted based on the availability of good pixels. Whereas, a non-noisy pixel is substituted with the weighted mean of the good pixels of the processing window. The experiments have been carried out on a large database for different classes of images, and the performance is measured in terms of peak signal-to-noise ratio, mean squared error, structural similarity and feature similarity index. It is observed from the experiments that the proposed filter outperforms (~1.5 to 6 dB improvement) some of the existing noise removal techniques not only at low density impulse noise but also at high-density impulse noise.

A Soft Parameter Function Penalized Normalized Maximum Correntropy Criterion Algorithm for Sparse System Identification

Abstract: A soft parameter function penalized normalized maximum correntropy criterion (SPF-NMCC) algorithm is proposed for sparse system identification. The proposed SPF-NMCC algorithm is derived on the basis of the normalized adaptive filter theory, the maximum correntropy criterion (MCC) algorithm and zero-attracting techniques. A soft parameter function is incorporated into the cost function of the traditional normalized MCC (NMCC) algorithm to exploit the sparsity properties of the sparse signals. The proposed SPF-NMCC algorithm is mathematically derived in detail. As a result, the proposed SPF-NMCC algorithm can provide an efficient zero attractor term to effectively attract the zero taps and near-zero coefficients to zero, and, hence, it can speed up the convergence. Furthermore, the estimation behaviors are obtained by estimating a sparse system and a sparse acoustic echo channel. Computer simulation results indicate that the proposed SPF-NMCC algorithm can achieve a better performance in comparison with the MCC, NMCC, LMS (least mean square) algorithms and their zero attraction forms in terms of both convergence speed and steady-state performance.

Performance analysis of improved iterated cubature Kalman filter and its application to GNSS/INS.

Abstract: In order to improve the accuracy and robustness of GNSS/INS navigation system, an improved iterated cubature Kalman filter (IICKF) is proposed by considering the state-dependent noise and system uncertainty. First, a simplified framework of iterated Gaussian filter is derived by using damped Newton–Raphson algorithm and online noise estimator. Then the effect of state-dependent noise coming from iterated update is analyzed theoretically, and an augmented form of CKF algorithm is applied to improve the estimation accuracy. The performance of IICKF is verified by field test and numerical simulation, and results reveal that, compared with non-iterated filter, iterated filter is less sensitive to the system uncertainty, and IICKF improves the accuracy of yaw, roll and pitch by 48.9%, 73.1% and 83.3%, respectively, compared with traditional iterated KF.

Evaluation of Direct Signal Suppression for Passive Radar

Abstract: Passive radar (PR) systems must be able to detect the presence of a target signal many orders of magnitude weaker than the direct signal interference (DSI). Due to the continuous nature of most PR signals, this interference, rather than thermal noise, determines the sensitivity of the system. Suppression of DSI and clutter prior to range-Doppler processing is crucial for maximizing the effective dynamic range, to increase detection range and improve overall system performance. A number of time-domain adaptive filtering techniques have been proposed to mitigate the effects of DSI, with varying levels of success. As such, an investigation of the primary factors affecting suppression performance is presented, using Advanced Television Systems Committee digital television (DTV) waveforms as an example, through simulation and extensive experimental trials. A number of spectral and spatially diverse DTV signals are considered to analyze suppression performance under a wide range of realistic scenarios. In particular, the fast block least mean squares filter is shown to provide good suppression performance with low computational requirements. Results of this analysis can be used to predict PR performance and stability. Practical metrics, such as suppression runtime and ease of implementation, also serve to counsel selection of DSI mitigation algorithms for experimental systems.

Optimized clinical segmentation of retinal blood vessels by using combination of adaptive filtering, fuzzy entropy and skeletonization

Abstract: Display OmittedThe block diagram of the proposed system. Occasionally certain areas in the retina can be questionable for physicians which can lead to wrong interpretations for patients.A method is proposed that introduces a higher ability of segmentation by employing Skeletonization and a threshold selection based on Fuzzy Entropy.By extracting indices of the human retina properly, physicians will be able to estimate pathological injuries with a higher confidence.The proposed approach is fast and outperforms over other previously competitive techniques.The proposed approach consists of two stages. First of all, the retinal vessels was preprocessed by the HSV space and Wiener Filter. Then, the segmentation level is implemented by using Adaptive Filter that employs optimum threshold based on Fuzzy Entropy and Skeleton algorithm. The analysis of retina blood vessels in clinics indices is one of the most efficient methods employed for diagnosing diseases such as diabetes, hypertension and arthrosclerosis. In this paper, an efficient algorithm is proposed that introduces a higher ability of segmentation by employing Skeletonization and a threshold selection based on Fuzzy Entropy. In the first step, the blurring noises caused by hand shakings during ophthalmoscopy and color photography imageries are removed by a designed Wieners filter. Then, in the second step, a basic extraction of the blood vessels from the retina based on an adaptive filtering is obtained. At the last step of the proposed method, an optimal threshold for discriminating main vessels of the retina from other parts of the tissue is achieved by employing fuzzy entropy. Finally, an assessment procedure based on four different measurement techniques in the terms of retinal fundus colors is established and applied to DRIVE and STARE database images. Due to the evaluation comparative results, the proposed extraction of retina blood vessels enables specialists to determine the progression stage of potential diseases, more accurate and in real-time mode.

Lorentzian Based Adaptive Filters for Impulsive Noise Environments

Abstract: In this paper, three Lorentzian based robust adaptive algorithms are proposed for identifying systems in presence of impulsive noise. The first algorithm called Lorentzian adaptive filtering (LAF) is derived from a sliding window type cost function with Lorentzian norm of past errors to combat adverse effect of impulsive noise on systems. The first and second order convergence analyses of the LAF algorithm are carried out in this paper. Then, to identify sparse systems in impulsive noise environment, $l_{0}$ norm penalty is introduced to the cost function of the LAF algorithm leading to a new algorithm called Lorentzian hard thresholding adaptive filtering (LHTAF) which employs hard thresholding operator with a fixed hard thresholding parameter to obtain sparse solutions. The effect of the hard thresholding operator is further analyzed, and the analysis shows that a variable hard thresholding parameter offers significant improvement in the performance of the algorithm, and this result in the final algorithm called Lorentzian variable hard thresholding adaptive filtering (LVHTAF) where the hard thresholding parameter is adjusted adaptively. Simulation results show that the LVTHAF outperforms the existing robust sparse adaptive algorithms by producing lesser steady state mean square error.